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In this work, we induce character-level noise in various forms whenfine-tuning BERT to enable zero-shot cross-lingual transfer to unseen dialectsand languages. We fine-tune BERT on three sentence-level classification tasksand evaluate our approach on an assortment of unseen dialects and languages. Wefind that character-level noise can be an extremely effective agent ofcross-lingual transfer under certain conditions, while it is not as helpful inothers. Specifically, we explore these differences in terms of the nature ofthe task and the relationships between source and target languages, findingthat introduction of character-level noise during fine-tuning is particularlyhelpful when a task draws on surface level cues and the source-targetcross-lingual pair has a relatively high lexical overlap with shorter (i.e.,less meaningful) unseen tokens on average.

Fine-Tuning BERT with Character-Level Noise for Zero-Shot Transfer to Dialects and Closely-Related Languages